        clojure.string/join
        (Integer/parseInt 2)))
 
+
 (def training-data (into [] (for [k (keys scrabble-values)] (char->bit-array k))))
+
 ;; convert the "labels" of the scrabble pieces (scores) into floating-point bit-arrays
 (def training-labels (into [] (for [v (vals scrabble-values)] (num->bit-array v))))
 


(defn evaluate
  [network]
 (classify network \j)
 (classify network \k)
 (net/evaluate network training-data training-labels)
  )


(defn train-and-evaluate

(def input-width (last (m/shape training-data)))
(def output-width (last (m/shape training-labels)))
(def hidden-layer-size 4)
(def n-epochs 100) ;;epoch is a pass through the dataset
(def learning-rate 0.1)
(def momentum 0.1)
(def batch-size 1) ;;For this example a small batch size is ideal
